plotly包是一个可以直接绘制可交互图表的R包，下面使用ggplot2中的diamonds数据做演示。
我们只需要将变量的名字映射给可视化的参数，plot_ly()可以自动尝试寻找合适个图形来展示数据。
# plotly tries to find a sensible geometrix
plot_ly(diamonds, x = ~cut)## No trace type specified:
## Based on info supplied, a 'histogram' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#histogramplot_ly(diamonds, x = ~cut, y = ~clarity)## No trace type specified:
## Based on info supplied, a 'histogram2d' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#histogram2dplot_ly(diamonds, x = ~cut, color = ~clarity,
colors = "Accent")## No trace type specified:
## Based on info supplied, a 'histogram' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#histogramplot_ly有大量的参数可以方便的调整你的图表，但是与ggplot2不同的是，如果你直接定义可视范围(如下图一),并不会得到你想要的结果，这里需要使用I()，声明该值位Asls。结果如下图二。
# doesn't produce black bars
plot_ly(diamonds, x = ~cut, color = "black")## No trace type specified:
## Based on info supplied, a 'histogram' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#histogram## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels# produces red bars with black outline
plot_ly(
diamonds,
x = ~cut,
color = I("red"),
stroke = I("black"),
span = I(2)
)## No trace type specified:
## Based on info supplied, a 'histogram' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#histogramplotly包使用了纯粹的分层图形语法，大量的函数都将plotly对象作为第一个参数。接下来使用一个layout例子来演示。
# modify
plotly::layout(
plot_ly(diamonds, x = ~cut),
title = "My beatiful histogram"
)## No trace type specified:
## Based on info supplied, a 'histogram' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#histogram然而这样的表现形式随着图形复杂度的上升，会显得十分臃肿。随意plotly包同样支持管道操作。
diamonds %>%
plot_ly(x = ~cut) %>%
layout(title = "My beatiful histogram")## No trace type specified:
## Based on info supplied, a 'histogram' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#histogram除了layout之外，还有add_*族的一系列函数可以将数据渲染为几何对象。比如：
# use add_* functions
diamonds %>%
plot_ly() %>%
add_histogram(x = ~cut)还可以和dplyr连用进行一些更加复杂的数据操作。
diamonds %>%
plot_ly(x = ~cut) %>%
add_histogram() %>%
group_by(cut) %>%
summarise(n = n()) %>%
add_text(
text = ~scales::comma(n), # 定义内容
# scales::comma 强制使用十进制显示数字,并三位添加一个逗号
y = ~n, # 定义文字坐标轴位置
textposition = "top middle",
cliponaxis = FALSE
)